Quantitative Comparison.
Table REF , REF  show that UCN surpasses most of the methods on all datasets and demonstrates the effectiveness of UCN.
It shows that the transductive FSL learning methods work better than the inductive FSL learning methods, indicating that query samples benefit prototype optimization. Note that the R12 backbone and pre-train strategy of the proposed UNC are most similar to FEAT [1]}. Compare to FEAT, UCN achieves significant improvement on miniImageNet and tieredImageNet in 1-shot accuracy.
Unlike UAFS [2]} which model uncertainty of the similarities of query support pairs, we propose a new way of computing uncertainty information of query instances by computing the mutual information of the augmented query instances.
Moreover, the obtained uncertainty information and the classification results of the query instances are used to guide the transductive inference process.
Relative to UAFS, our prediction accuracy increased by a large margin on all metrics. In addition, we obtain competitive results in a semi-supervised setting and present the results in supplementary materials.
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